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Interpretable Machine Learning with Python

You're reading from   Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800203907
Length 736 pages
Edition 1st Edition
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Author (1):
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Serg Masís Serg Masís
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Serg Masís
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Machine Learning Interpretation
2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER 3. Chapter 2: Key Concepts of Interpretability 4. Chapter 3: Interpretation Challenges 5. Section 2: Mastering Interpretation Methods
6. Chapter 4: Fundamentals of Feature Importance and Impact 7. Chapter 5: Global Model-Agnostic Interpretation Methods 8. Chapter 6: Local Model-Agnostic Interpretation Methods 9. Chapter 7: Anchor and Counterfactual Explanations 10. Chapter 8: Visualizing Convolutional Neural Networks 11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 12. Section 3:Tuning for Interpretability
13. Chapter 10: Feature Selection and Engineering for Interpretability 14. Chapter 11: Bias Mitigation and Causal Inference Methods 15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability 16. Chapter 13: Adversarial Robustness 17. Chapter 14: What's Next for Machine Learning Interpretability? 18. Other Books You May Enjoy

Quantifying uncertainty and cost sensitivity with factor fixing

With the Morris indices, it became evident that all the factors are non-linear or non-monotonic. There's a high degree of interactivity between them – as expected! It should be no surprise that climate factors (temp, rain_1h, and cloud_coverage) are likely multicollinear with hr. There are also patterns to be found between hr, is_holiday, and dow and the target. Many of these factors most definitely don't have a monotonic relationship with the target. We know this already. For instance, traffic doesn't consistently increase as hours increase throughout the day. That's not the case between days of the week either!

However, we didn't know to what degree is_holiday and temp impacted the model, particularly during the crew's working hours, which was an important insight. That being said, factor prioritization with Morris indices is usually to be taken as a starting point or "first...

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